[R] ANOVA-like tests of geometrically-distributed data

[Robert Merkel]

I have a statistical problem which has given me no end of grief recently, 
and am posting here in the hope that somebody can give me a straight 
answer.  I'm a IT postgrad, not a statistician, so people may have to 
speak really slowly and clearly for me to get it :)

I am collecting simulation data, and the results are geometrically 
distributed (or approximately so).  From what I can gather from my stats 
books, provided the sample size is large enough (>30 or so) I can use t 
and z-tests to compare means under different experimental conditions as 
the CLT says that the sample means will be approximately normally 
distributed.  

However, also as I understand it, the ANOVA explicitly assumes that the 
population is normally distributed, which is an assumption that in my case 
is not satisfied.

I have also been told that something called a "generalized linear model" 
can be used to perform ANOVA-like statistics on geometrically-distributed 
data, but not how.  

There is R documentation on a function "glm" and also "anova.glm" which 
discuss stuff that looks vaguely like what I want to do, but I can't 
really make sense of it.

Can these functions do what I'm trying to do?  If so, what's the 
procedure?

Any help will be *much* appreciated.

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